Data Driven Real Estate Podcast #26 – Business Intel with FRED and the Federal Reserve Bank of St. Louis #DDRE26

The Data Driven Real Estate Podcast #26 – Business Intel with FRED and the Federal Reserve Bank of St. Louis #DDRE26

FRED is a data service provided by the St. Louis Federal Reserve. FRED is updated daily and allows 24/7 access to over 800,000 financial and economic data series from more than 100 public and proprietary sources. From kindergartners to economists to small business, the vast amounts of data allows us all to make more informed decisions. 

This week, we welcome Yvetta Fortova, the manager of FRED and FRED-family products (ALFRED, GeoFRED) in the Research Division, where she oversees the collection, organization, and publication of data found in FRED.  We also have Maria A Arias who works on the FRED team developing and maintaining the data update process. She also helps lead FRED workshops for new and existing audiences to market new features and help users make the most of FRED. 

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Show Topics

  • 00:00 The Data Driven Real Estate Podcast welcomes Yvetta Fortova and Maria Arias from the @Federal Reserve Bank of St. Louis Federal Reserve Economic Data (FRED) team
  • 02:16 When was FRED launched?
  • 03:34 How many data time series are housed within FRED?
  • 09:36 What is GeoFRED?
  • 12:03 How big is the FRED team dealing with 800,000 data series?
  • 16:20 Kindergarders use FRED?
  • 18:56 Who decides the data sets that are featured in FRED? Who are some of the data partners?
  • 33:23 How frequently is the data updated?
  • 39:40 Does access to data at FRED inspire others, like states, to do a better job curating their own data?
  • 47:45 Example of a real estate investor exploring a completely new market. What kind of data is available on things like the economy, demographics, and real estate?
  • 57:10 What is FRED working on for 2021?

Show Transcript

Aaron Norris  00:05

Welcome back to the Data Driven Real Estate podcast, the podcast for real estate professionals dedicated to driving business using data. I'm Aaron Norris, and along with co-host Sean O'Toole, CEO of PropertyRadar, this is Episode 26. And we get to interview FRED. FRED is housed within the St. Louis Federal Reserve and we are very excited to interview as we've been longtime users and huge fans for a number of years. This week, we've got Yvetta Fortova, she is the manager of FRED and FRED-family of products, including ALFRED and GeoFRED, which I didn't even know existed until this interview. We also have Maria Arias who works on the FRED team developing and maintaining the data update process, the data process on hundreds of thousands of different data series. We cover everything on what that's like, and the different data sets that are available, how they inspire everybody from a kindergartener to a Ph.D., how to explore the data and make sense of it and release their own research, and how you could even become a forecaster and get worldwide recognition. You won't want to miss this week. So, hey, Maria, and Yvette, we really appreciate you being here today. And I guess the question I want to start with is why data? What keeps you excited about this industry? What do you love?

Yvetta Fortova  01:18

Hello, Aaron, delighted to be here. Just before we start, we would like to say that the views expressed here are the views of our own and not the views of the, or the, of the Federal Reserve Bank of St. Louis, or the Federal Reserve System. But data is very exciting to us. And we work on a FRED team, which is a team specializing in the dissemination of Federal Reserve economic data. And really, data is very unique because we really like the fact that our website can deliver data service to users. And data can mean stories. Because when you look at data, you can see trends in data, you can see when the data goes ups and downs. And that always comes with an interesting story.

Sean O'Toole  02:16

I've been a longtime FRED user, you know, probably since it first launched, when did it first launch?

Maria Arias  02:26

Yeah, that's a really interesting story, actually, officially on the web, FRED launched in 1991. But the ideal FRED actually started in the early 60s. So, the research director at the time name was is Homer Jones. He wanted to share some monetary data with other policymakers, but also with the general public. And so, he started disseminating a memo that contained these three data tables. And so, for a long time, it was just like a memo that was updated, of course, like once people received that they wanted to keep receiving it with an updated value every, every month, or every week, however often this was. And eventually, in the early 90s, FRED became a dial-up bulletin board, and it's just grown from there.

Sean O'Toole  03:18

Wow. Okay, so, I'm not nearly as early as I thought I was on that.

Aaron Norris  03:25

And when you say grown, can you describe sort of the vast amounts of data? Like in your library?

Yvetta Fortova  03:34

Yes, FRED has definitely grown exponentially. We've, as Maria said, in 1991, when FRED first started, we had about 30 times series. And when you talk about time series, think about the unemployment rate or gross domestic product, those are the type of time series that were available. And then we've really, with technologies and with a lot of automation. In 2013, we reached our milestone with 100,000, 100,000 series. And today, we are almost at an 800,000-time series that we continuously maintain and update in FRED.

Sean O'Toole  04:22

Just again, I want to keep like help orient our readers, right? So, this is basically a service of the St. Louis Federal Reserve, right, that makes that available. And it's at FRED.StLouis, tell, tell us where people go to accesses if they haven't before.

Maria Arias  04:41

Yes, FRED is a data aggregator, which means that we get data from public websites like government institutions, international organizations, and then some private institutions and academic resources. And we keep that up to date in our website, and so the way that This works is we're putting all of this data in one place for users to be able to combine and compare different time series from different sources. So for example, if you want to compare employment growth and economic growth using parallel employment and the gross domestic product, those are, both of those series are produced by different government agencies, the Bureau of Labor Statistics and the Bureau of Economic Analysis. But in FRED without leaving our website, you can plot both of those in a graph very quickly, and then compare them immediately. And again, for everyone who has not visited FREDyet. It's that FRED st Louis

Sean O'Toole  05:45

Perfect. Yeah, that's great. One of the other things that's, you know, you know, really impressive to me about it. And it's not once it's perfect, right, like so. And maybe you could talk about this, but you're getting this data from all these different sources, right, and now you're gonna lay it on a chart and compare it. And that presents as a data guy that presents some real challenges, right? Are you getting the same time slices, you know, quarterly versus monthly versus annually. And how much of your work goes into just getting these things to align so that, they work on a chart together.

Yvetta Fortova  06:19

So over the years, we developed a process where the notion of the time series is well defined in our back end. And really, it's all dependent on the frequency of the data. So you can have data, like interest rates, there are your interest rates, who are published, mated with a daily cadence, and the data is on a daily frequency. And then you can have data like labor markets, which are published by the Bureau of Labor Statistics, and those data are monthly. And really, when all of this gets put together on our website, users can combine these data together on a graph. And there is a kind of like magic in the, in the bag that happens that is able to translate that this data, while it's the treasure data, while it's daily, and the labor data, which is monthly can end up on one graph. And we have highly interactive graphs, so users can see their values and download the data from the graph. And really, that is, what makes FRED very unique is the fact that we are trying to provide this data service to the public and, and recognize the value in being able to give up to date information to our users. Right. And

Maria Arias  07:45

just to add a little bit to that some of the tools that we provide, as well, while you can add multiple series to the graph, you can also edit the graph right then and there. So for example, if you want to compare, like you mentioned quarterly and monthly data, you can convert the monthly data to a quarterly frequency, as well. And so that's all the calculations are all done in the background. And then you can pick if you want like an average for those monthly values converted to a quarterly frequency, or if you want a different type of aggregation, things like that. So it can really allow you to create customized graphs for whatever your needs are, or whatever data you're interested in.

Aaron Norris  08:24

How did this line at the St. Louis Federal Reserve, or the other banks doing something similar, or they're just like St. Louis has got this?

Yvetta Fortova  08:33

So spread originated in St. Louis, and we are part of a research department. And as Maria kind of diluted that, really the in the 60s, the big vision for Homer Jones, who was a research director, at that time, he really liked the vision of allowing data to be available to the public and really, this product has since been organically evolving over the years with multiple other side products and creating this FRED FRED-family of products so FRED is the mainstream of the data but then we have a mappable data in the in a geoFRED and then we have also real-time data in our FRED so we're really trying to capture as much as much pieces and angles of the looking at the data as possible. It's

Sean O'Toole  09:36

somehow I completely missed geoFRED like I've been a FRED user for I can't tell you how many presentations of mine have at least one FRED chart in it, if not quite a few. So um, but I completely miss geoFRED. I just found it this morning. And I only got a chance to play with it after Aaron mentioned it. And so I don't know how I possibly missed that. How long is that we've been around?

Yvetta Fortova  10:05

Quite a bit. We.... and what geoFRED is just a way for users to see cross-sectional comparisons. So it's just a way of looking at the data on a map. So in comparing the States and in the US, for example, and if you're right that it was developed on a side, and maybe it was not as well as well provided to our users, but we have been trying to over the years trying to incorporate the maps on the FRED website in a better way. So nowadays, if users would be able to see the View Map button next to their graphs, that's an indication that they can also examine and then analyze the data on a map. And in addition, we also provide globes images of globes below the graph, in the related content section where users can also see data in geoFRED.

Sean O'Toole  11:10

Okay, visually, I was trying to do I'm sorry, Aaron, you go ahead.

Aaron Norris  11:14

Visualizations are just very powerful. So I have a lot of playing to do. I too have been a long-term user of FRED and pulling together different data from different governmental agencies has been very tricky. And then I found FRED and it was a godsend. I can't tell you how much time you guys have saved me over the years.

Sean O'Toole  11:34

We're total fanboys just to be clear. We're definitely your fanboys.

Aaron Norris  11:39

Sean, I already asked Maria. I'm like, Where can we get those sweatshirts?

Sean O'Toole  11:43

I know I totally.

Maria Arias  11:46

Yeah. The FRED swag is available at the St. Louis Feds, the economy museum. Unfortunately, it's closed right now. But as soon as it opens, we're expecting to have an influx of FRED fans coming to get some FRED swag.

Aaron Norris  12:03

I look how many how big is your team, like when you're dealing with 800,000-time series? What kind of manpower does that take?

Yvetta Fortova  12:14

Our team is, is relatively small, we have a team of about 10 people. And they consist of the developers who are working on making sure our website functions properly and developing new features and new tools for FRED. And then the other part of our team, our data engineers who specialize in making sure our data and content in FRED are, is up to date. And because FRED is so popular, we also have a lot of help outside of our team within the research department to help us with other things related to data and, and content.

Aaron Norris  13:01

Do you have any idea how many users are on your site any specific time of year?

Yvetta Fortova  13:10

Guess our users fluctuate. And we have kind of like seasonality in our, in our user and our users. And that is correlated a lot that education of education and semesters at schools because one of our core users are academia, professors, and students who utilize our product to learn about the economy, money, and banking. FRED is in textbooks for students to learn about data and do homework. But overall, we do have over millions of users annually that come to our sites from many countries and are hungry for data and for information about data and what and graphs.

Maria Arias  14:08

Yeah, something that we think is really cool about FRED new services. At any point in time, we could have a person who is just now learning about economics, it's their first time looking at a graph, as well as a Ph.D. economist or even a Nobel Prize-winning economist on the website. So again, we try to make sure that our features are easy to use, the data is accessible for everyone to learn about, but also that we have tools that more advanced users can take advantage of and also, you know, like everyone in between. So we're, again really happy to hear when people really like our product, and we try to keep developing new features for you guys out there who are using it to collect the different data that you need.

Sean O'Toole  14:57

I personally think it should be you know, there's A whole class on it in high school, maybe elementary school, right? Like for every person before they're allowed to graduate before they're allowed to vote, like they should know how to use FRED, like, it would clean up half the crazy ideas that I see on Facebook and Twitter, and all the wrongheaded ideas out there. Because it's just such, you know, you can ask these questions rather than, you know, guess about them. And it's just, it's really an amazing resource.

Maria Arias  15:29

Right? Even taking one step back, one of our core missions is to promote data literacy. And that's something that is very important to us. So just going back to understanding, you know, what is the frequency of this data? What is what are the units, you know, how can you compare two different series, they have to be in the same units in order for you to compare apples to apples and not apples to oranges. So we work very closely with another group in the research department. That is the Economic Education Group, and they create a lot of different materials from kindergarten all the way to high school and college-level educational resources for students out there and teaching assistant teaching materials as well.

Aaron Norris  16:17


Sean O'Toole  16:17

That's amazing. I know that that's, that's great to hear.

Aaron Norris  16:20

I'm gonna have to find links to that. That sounds really cool. Kindergarten?

Maria Arias  16:24

Yes, not to kindergarten, there are some actually their stories that are read out loud. And so you could just sit your kid in front of a tablet and play this story that teaches them a lesson about finance, or education or economics, things like that and find resources at econ low

Sean O'Toole  16:44

Might have to start with that kindergarten, one for my 18-year-old.

Aaron Norris  16:49

And build your way.

Sean O'Toole  16:50

That's amazing. All this way up? Yes. He's actually pretty good. He's had to put up with me as his dad. So he's not that bad.

Aaron Norris  16:57

Now, somehow, I don't think he's gotten away with scot-free for sure.

Sean O'Toole  17:00

No, yeah.

Aaron Norris  17:01

Maria, you have to deal with a lot on the back end. And I really want people to appreciate what it means on the data side. So what is your day-to-day look like when data is coming into the system? How does that work?

Maria Arias  17:13

Right. So for our process, currently, a lot of it is automated. And so we have developed a Python-based pipeline where when the data comes out, we reach out to the sources website, collect that data and process it and save it onto our database. And at that point, it's available immediately on our website. But we have a set of like notifications and things like that throughout the day with these processes, making sure that everything in the data is updating successfully. And if there are issues, we would go in and check each like specific data set or release to see what is happening. And then this is kind of for, like maybe 90% of our data is automated, updated in an automated way. And then we also, for some, like for the remainder of that we either manually have to go and check to see if the data has updated on the sources website. Or maybe we wait to receive an email from the subscription, that the data is available, things like that. So there are a few kinds of outliers out there that involve some sort of manual intervention to get the data updated. But kind of even before we get to that stuff, as we add new data to FRED, we have to add them to this data pipeline. So that involves us, as data engineers understanding the data and how the data is provided by this particular source or the particular data set that we're adding, so that we can process that data and kind of standardize the data so that we can save it to a database,

Aaron Norris  18:55

Who decides what data gets to be featured in the app?

Yvetta Fortova  19:01

So for us, we have a lot of a long list of data requests that come from various places, our users, the data providers themselves, who would like their data edited to FRED, and as we mentioned, our team is relatively lean. So what we do is we have internally have a committee that meets on a regular basis. And then we select data that we think are relevant to our users, and they are reliable as well from the originating sources. So that we we can continuously provide new content to users and we can give our users additional data to work with. So for example, Recently, we have added weekly pandemic unemployment claims, which have been developed by the Department of Labor. And we have also worked on the addition of housing data, from optimal blue and, which are also available on our website. So anybody who's interested, can go and find them.

Sean O'Toole  20:29

I saw one on geoFRED it was market hotness. That seemed to be a real estate-related.

Maria Arias  20:38

Yes, but as a data set from, as well, they have a set of indicators that they define as market hotness indicators. So it's a different topic that kind of like how many days a house has been or like, an average of how many days houses have been on the market in this region, things like that. And so that's a data set that they put together and we publish in FRED.

Sean O'Toole  21:08

On geoFRED, it had very few counties populated from what I saw. Yeah. Is that still just a work in progress? Is that the reason or did they just not have data on most of the country? Or do you have any ideas there?

Maria Arias  21:19

Yeah, so that's we have their complete dataset. And FRED for the market hotness data, they have only the largest. I don't know how many I think it's the largest, like 100 MSA. And then I think the counties that are included in that data set are from those MSA and surrounding areas as well. So it's not a county by county for the US for this particular data set.

Sean O'Toole  21:44

But we do happen to pull down County, if I pulled down MSA, it probably would have filled in a little nicer for those Msh. Two versus County. It's like only when the MSA maps to the county, would it work? Right.

Maria Arias  21:56

So yeah, it depends on what data they have available for that particular data? And yeah, I know that for that one. In particular, it's just the certain number of the largest MSA and then those surrounding counties.

Sean O'Toole  22:10

We have a lot of data users. And I want to come back to your data pipeline, right. So in that in for that data pipeline is, you know, I've used obviously, the website and the graphical tools, right? folks, that want to get access to raw data? Is your data pipeline, open-source? Do you guys have an API to the raw data? What are the Is there any options there, they've got to go do all the same hard work you did?

Maria Arias  22:40

Right. So the work that we do, hopefully, will make everyone else's work a lot easier. We are doing the hard work of going to, you know, the census and the BLS and all of these other organizations and parsing their data that is provided in different formats. And putting it all in like one standardized format and FRED. And so for all of you who want to maybe automate your workflows or things like that, we also have, we have a FRED API. And we also have an add-in for Excel that you could use. So that allows you to, again, automate your workflows in different ways and combine it with the other data that you already have. So if you like programming for the API, in particular, you just have to create a free user account and request an API key. And there is third-party kind of wrappers or packages that are already out there that other people have created. And they just kind of wrap around the fret API and make it easier to use. And then that's in addition to you know, if you go to the Ford website, you can download the data as a CSV or an Excel file. And even if you've added multiple lines to the graph, if you download the data from that graph, it'll include all of the data that you have. So that's also another way that you could access the raw data. And then all the above love it. Awesome. And then you have for the Gioffre data as well. If you want to download all of the say, like all of the county-level data for the entire US, that's probably the best way to download all of that data is through geoFRED for that particular map.

Sean O'Toole  24:21

And all the data itself, basically at least is provided from you is open source, and people can use it in their applications and that kind of stuff for their license limitations at all.

Yvetta Fortova  24:33

So most of the data we have come from governments or sources or international organization organizations, and

Sean O'Toole  24:43

public record

Yvetta Fortova  24:44

public domain data correct. But we do have a small percentage of data that comes with copyrights. And users will be able to identify those data because we have a node under the graph that specifies What the copyright, the restrictions are. And we also have in a note, what that means to the end-user. And if they need any additional actions to utilize the data, but overall, from the whole scheme of data you have in front, it's a small percentage of data.

Aaron Norris  25:23

I like to vet what you said about data telling stories. And part of that is what I like about data is there's a trust factor to it. It's it's just the data, the data is the data, data doesn't get political, it tells a story. How important is the data vetting process, when you consider new partners? I was I had no idea that you were pulling, pulling in like a national association of realtors, which is an association group. What does that data vetting cycle look like?

Yvetta Fortova  25:53

When we are looking on where like, what kind of sources, the data come from? We are looking on? How much data would be available to us? And also how what the data look like how is the data constructed? If it's data that is academic data, we are looking for a ways to see whether there is some peer review work and or paper that has been published for construction of the data? And also, if there are ways that this data, how is the data disseminated Oh, no. So in what way we, as a team can maintain the data, which is also a big piece. Because we are trying to look for ways of on getting the data in an automated way. And cut down on the manual work. So there are pieces of of that. And obviously, it also comes down with private sources to having proper licensing agreements in place.

Aaron Norris  27:05

I know you've been on the research team before. So do the data sources come in? And then your research team gets a list of all the new data sources and they dream up? What's next? What triggers some of the research that your team does?

Yvetta Fortova  27:20

So if we didn't honor, they didn't our team we do like our team does not do research. We're just solely doing breathing and living from FRED. Yeah. But the economists who are in the research department, they pretty much have really a choice in whatever they would like to publish. And, and write about. And their process. I'm not familiar with their process in details, but

Aaron Norris  27:58

that's okay. I was just curious, like, I can't imagine having access to 800,000 data series, and you just wake up in the morning like, Huh, what comedy work I got to put together today. That's amazing.

Sean O'Toole  28:10

So do either of you have any favorite data series?

Maria Arias  28:16

It's a it's hard to pick with so many of them.

But I didn't have

Sean O'Toole  28:22

to look at in the morning or when the new drop comes that like, Oh, that's the one I want to see. Yeah,

Yvetta Fortova  28:30

we've definitely become kind of a data nerds over the years working with the data. And there, some some data are definitely interesting to talk about. For example, we do have data in fret dead is the longest series that we publish, and it's historical data for population of England, that goes back down back to 1086. So it's really depicting a plague that has that impacted population. And then you have some really interesting data on orders of sinks, kitchen sinks and toilets during the industrialization period, and the the peak in indoor plumbing that has happened during that time. So really, those are, those are our kind of like, nice dimensions. But really, when it comes to favorites, it changes all the time because we data is really interesting, when there are trends and when there are changes in the data as you as you deleted it to the to just spikes and drops in data. So really looking at business cycles and seeing how the data is Changes fit the business cycles or after the business cycles are, I think the most the most interesting part of looking, looking analyzing the data.

Aaron Norris  30:10

Data is such a hard business to and I'm sure, as you've made more available, a lot of people are very demanding, I think one of my favorite series has always been migration. And especially with COVID, you hear a lot of there's been a lot of talk in the real estate space and different data series to track not having to wait for a census, are you guys getting a lot of pressure to provide more series that are a lot more timely, instead of waiting for government, census and things of that nature.

Maria Arias  30:39

We've seen a lot more demand for more, not only more timely series but also data that are more frequent or that have a higher frequency. So say daily or weekly data instead of quarterly or annual data. And so, especially this year, when, you know, if you think about GDP and the state of the economy, you have to wait for the quarter to be finished. And then one more month for the initial release of the previous quarter's GDP to come out, right and ask like things are changing in the country, it's hard to tell what the state of the economy is at that point. So we've definitely seen an increase in demand. Unfortunately, we're also kind of bound by, you know, we want to continue to provide high-quality data to users. And we can't just add everything right away either. And so it's been kind of a, trying to see what the data providers are creating for like new releases, for example, the census created they created an experimental data set from their quarterly business formation data, they started calculating weekly business formation data. And as soon as they started making that available, we added that to FRED in the summer or early summer, late spring, something like that. And so now there's this weekly level, business formation statistics that has been really, really popular.

Sean O'Toole  32:12

Is there what's the geo-granularity on that?

Maria Arias  32:16

Um, that is available at the state level? I'm not sure if it's also available at the MSA level.

Aaron Norris  32:22

It's so funny, Sean. We were just talking about that with Doug Duncan.

Sean O'Toole  32:25

Doug Duncan, Chief Economist for Fannie Mae was just, he was just saying that's the one data set he would most like to have right now, but really, probably more down at the county or MSA level? Was I'm gonna have to dive into that one, for sure. And we'll have to share it with Doug as well. Yeah, so out of you, you know, you mentioned 800,000-time series, like, give us some idea, you know, a guess is fine. But like, what's the fastest data set? You're getting? You're not like a Bloomberg terminal where it's, you know, sub-second updates on stocks or anything like that. But the fastest is maybe daily, hourly? And then the worst is, is annual or is it some of them even less often than that?

Yvetta Fortova  33:17

Oh, are you? Are you referring to how quickly we can get the data to FRED? Or what kind of? Well,

Sean O'Toole  33:23

I think there's two separate questions there. And let's do both. Let's do the first one is how often, right? So out of your 800,000? How many of those are annual versus quarterly versus monthly versus daily versus hourly? Right, just a guess. And then, and then, you know, she mentioned, Maria mentioned the, you know, some of these things have a month delay. And so both of those things are really interesting issues, right, like, that I don't think people think enough about we have to think about it a lot, because we provide public records data on real estate, right. And in a small county, we might go get the data every other month, right. And in LA County, which is the largest in the United States, right? We have stuff, you know, very, very quickly, so we get it daily. And, you know, we usually have it published to our site within just a couple of days. Versus Alpine county might get it every two months still publish to our site within a couple of days, but there are big differences there. So, can you speak to that kind of distribution among your datasets?

Yvetta Fortova  34:30

The data we have in FRED are anywhere from daily, weekly, monthly, quarterly, semi-annual, and then we have some five-year frequency data. And really, the distribution among what kind of data we have in FRED is more or less; we have most data for on annual frequency and then it kind of goes down from annual to quarterly, monthly, And daily. So that is kind of like the distribution of the data we have in FRED. But then there are also aspects of how, how, how the data updates. So when we do our updates, let's say that employment data, the employment situation that is published by the Bureau of Labor Statistics is published on the first Friday of every month. And it comes out around 730 Central times. So we really are trying to hit the update process, immediately or minutes after the data is released. And then obviously depends on the bit on the size of the data itself. So if the employment situation has couple of 1000 series, it takes some time to get all the new information into FRED. But we are trying to optimize our process to really get into it in minutes or from what the actual release of the data happens. And then there are also aspects of the data itself. So when the data is released, there are there is a lack of the information because what happens is, first Friday of the month, there is a publish publication of the data. But actually, the data that's published represents the values for the previous month. And then that goes all the all the way back down to the origin eating agencies who are working on the on obtaining the underlying data, and perhaps the micro data. So they need time to get a good size sample of the data, which then allows them to model the data and make the aggregated values available. So those in most data, if we have in FRED of work on on this typical lack where you're not going to see data for December, monthly data for December, if the December isn't over yet. And again, it comes back down to, you know, the originating agencies having time to prepare those estimates. On the other hand, we also have a little bit of advanced data or data that is forecasting that are looking into the future. Which Users can also use to look at what the data will look like. And last not but least because all of this process of disseminating those data and publishing data is really based on a lot of times on surveys and on incomplete samples. That's why agencies over time, as they are publishing new values, new monthly data, let's say they are also going back and revising existing data. And for that reason, we also have Alfred, which is an archival, FRED.


No, interesting.

Sean O'Toole  38:19

Yeah, no. And that was Alfred was another one I wasn't familiar with. And, you know, Aaron mentioned this morning when we were doing our pregame, you know, prep. And, you know, he mentioned he had a data series that changed quite dramatically, you know, and one of the things that he was following and, and he was able to find that the historical data and ALFRED and see why, you know, see that difference. So that's, that's pretty cool that the archival piece too valuable.

Yvetta Fortova  38:49

Yes, I think the best example that we like to give is from vit gross domestic product. So if you think about the gross domestic product, the data itself, R is a quarterly frequency data, but then the values are annual, annualized. And then the data updates monthly, and then the bear values to revise could revise every month. And on top of that there, there is an annual revision that happens every year in summer. And on top of that, every three to five years, there's a benchmark revision that may change everything under the GDP.

Aaron Norris  39:32

I did not know that.

Sean O'Toole  39:33

Wow, GDP doesn't mean anything. It's just a random guess. Just kidding.

Aaron Norris  39:40

I do you find that your work has inspired states to get a little bit more serious about collecting data. I noticed just in California as an example in the last year, I've noticed a lot of data collection sort of budget line items in legislation talking about digitizing and starting to track things. And Can Can you point to some of your work being so helpful that states are like we got an upper data game,

Maria Arias  40:08

we would like to think that we inspire other government and public institutions to improve our data collection. In some cases, where especially like smaller government institutions have reached out to us about, you know, having their data and FRED and it's not necessarily available in a machine-readable form, we've been able to work with, with them, and even some of the academic data that we've added, that seems to be pretty popular. And FRED, we've been able to work with them to help them put their date make their data available on their website in a, an easier form for us to parse but also for other people. So like machine-readable forums, standardized tables, things like that. And so we have seen a little bit of that. But we don't work with like, we don't work with every government institution out there that we collect the data from, because again, all of this data is publicly available. So we just collected from their website. But we would really hope that more and more, especially smaller government entities, to collect their data and make their data publicly available in an easy format, because it's not only for us to provide a service for other users, but also, you know, everyone around them, and in your communities, you know, you know, what's important to your community. And you know, what industries are what drive the community forward, things like that. And so if the government's local governments were able to help collect that data and make that data more available, it would be easier to also analyze the state of the economy and these kind of smaller geographical regions, like you were talking about earlier, it's really nice to have county-level data, it would be even nicer to have it at a smaller geographical level for everyone who's doing research and trying to understand what's happening.

Sean O'Toole  42:14

Our company, the one that sponsors, the Data Driven Real Estate Podcast, property radar, really only exists, because of how difficult it is to get public records data on real estate assessors, data recorder data, you know, etc. And I would, it would, it would make me so happy to shut this company down, because the data was directly available. On an easier format, like out of all the things that we spend money on as a government, you hear billion dollars here a billion dollars, they're like, why your group and the group's in all these different levels aren't funded to the tune of billions of dollars? You know, I have to say, I was a little dismayed to hear how small your team was, and I kudos for what you guys accomplished with that team. But, you know, if you ever need somebody like you to write a letter on why you should get more funding, like you can count me in.

Aaron Norris  43:15

Yeah, the actionable side of data I serve on the county board for 211 out here, and you know, a lot of counties will do is to one one, Aaron, oh, sorry, two, and one is a Health and Human Services hotline. It's, it's nationwide, it's sort of like the 411 for help. It's it has a lot of nonprofit data government assistance programs. And I think why I joined was the data is that you don't have to wait a year for to spend $100,000 on what people need in the community. This two on one hotline is tracking the need. So 30% of the need in our community for the last three years has been consistently been housing, you don't need to spend $100,000 in wait a year, it's almost immediate. So it's it's data does tell a story. And sometimes it's really important because government entities can redirect resources, where it's needed, instead of wasting time, and you're a year late. So I'm just curious, what are some of the data that you're most excited about in the last few years that you've seen cross your desk that you wish people knew about?


So since 100,000, there's too many

Maria Arias  44:24

what we've added this year, um, something that we did this year, and it's real estate related, so I definitely wanted to bring it up is the optimal blue mortgage market indices. So they have mortgage data that is broken down by different FICO scores and by different types of loans. So that's a, again, another private institution that reached out to us to have their data added to FRED and it's a it was added this year. But then we also have some other like you've mentioned the weekly pandemics claim pandemic claims data. That was a really interesting one. We also added daily FOMC target rates that were digitized by one of our economists at the St. Louis Fed and some of his co-authors going back to the early 1900s. So now you can get, like FOMC, target rates starting 1900s. And then like slowly put the time series together, up until the present day. And then what else? Oh, there's another one by the group of economists this one and this is going back to kind of some of the changes that we've seen this year have more demand for high-frequency data is a group of economists created the weekly economic index by last names of Louis Martin stock. And this is a kind of like a now cast, but not necessarily an outcast, per se. But they combine several high-frequency data that are available at the national level, and they create like a GDP forecast more or less at the weekly level. And that is actually updated twice in a week. And so that's been really interesting to see as well. And again, just kind of making kind of having like a more frequent update of state of the economy and keeping that conversation going.

Aaron Norris  46:33

Is it… I haven't looked yet, HMDA data I know is a, I'm a, I have my mortgage license. And the amount of detail that the CFPB has mandated that we start collecting at the loan level is quite expansive. Is that available on FRED yet?

Yvetta Fortova  46:50

Oh, we do not have HMDA data in FRED. Okay, but I would say for anybody who is interested to learn about what data we have added to FRED, we would recommend signing up for FRED newsletter, which is in bottom of our web pages, and we let users know what new changes in data and what new data we edit that way.

Sean O'Toole  47:19

Right there. That's my tip of the whole podcast, right? Like, because I've been a FRED user forever, and I'm not signed up. And I don't know why. So I'm signing up right now.

Aaron Norris  47:31

I'll make sure to post some extra links to make sure in the show notes that people can find this stuff really easy. So I think in the real estate space, there's a lot of like exploring different economies, and maybe some people they've never approached FRED before. And so maybe we can give a little bit more industry-specific. And I know this is your personal opinion if you were going to explore a state that you didn't live in, and you were interested in, you know, the economy, real estate, I don't know what kind of things would you explore in FREDpeople might not know to exist.

Yvetta Fortova  48:05

So definitely, for someone who is new to FRED, we would say, to go and try to search for information and see what kind of data we have available for given states or Msh, or counties. On the regional level, we'd enable quickly realized that we do have a lot of major economic indicators like labor data and gross domestic product. But then we also have data on property prices, like house price indices, and we have rates on mortgage rates. So there's really a wealth of information available. And it's a, it's a really matter for people to go and try it and see what we have, you know, from housing stars and consumer price index, to prices and wages. And it's kind of like, when you go shopping, and you feel like you've really good, got a good bargain at the end of the day. So that's really what we are trying to give users with FRED.

Maria Arias  49:19

Right. And then I would just add on to that, that if you find some graphs that you find really interesting, or that you would like to come back to. This is just kind of a plug for some very useful FRED features. And especially if you don't have a FRED user account yet, right at the bottom of the of the graph, you can save graphs to your user accounts. But then you can also add graphs to a dashboard. So as you add graphs of say, a similar topic that you are saving to your user account. You can also put all of those graphs or maps onto a dashboard and add other like snippets, like notes or like a single number or something like that. And then all of these graphs can be saved so that they are updated automatically. So the next time the data comes out, you just go back to your dashboard or to your saved graph, and the data is automatically updated for that. So this is a very useful feature that if you're not familiar with FRED, or if you don't have a user account, I highly recommend you check out, especially again, if you have these kind of several graphs or indicators that you'd like to go back to. And then together with that, you can make your dashboards public and share them with your colleagues or share them with other people in the industry, to just, again, simplify sharing data and sharing content.

Sean O'Toole  50:43

But, you know, one thing I haven't looked at, and I'll just ask is, can you embed? Can you get like an embed code for those graphs, so you can embed them in a website or whatever, so that they're constantly updated on your site as well?

Maria Arias  50:55

Yes, just below the graph, there are some shared links, and account tools. So under the account tools, that's where you can save those to your account. But then the share links, you can share a URL to a graph. And so whatever modifications you've made to the graph, and you share that, using the URL provided under that button, the person who accesses that will see exactly the graph that you created. And then same with the embed code, if we provide like a pre created snippet that you could just put onto your website and embedded that way.

Sean O'Toole  51:30

In my, you know, one of the things I really like is the bars that show recessions, right, um, and, excuse me here, if I like don't know, that, in terms of those bars, that kind of underlie right recessions is, is that the only one or there are some others.

Yvetta Fortova  51:51

We only have recessions at this point on the graphs. And there are other ways you can, you can take those three sessions off from the graph, if if there's no need for them. But we also have a way to manipulate the data of it in the graph itself. So for example, if you have, if you have a data, you can then index the data to different times of the year or different period in time, and it will set the values to 100 of a given period. And then you can see changes to the in the data. So that is also one way how someone can kind of take the data and make it work to whatever they are trying to save at the data.

Sean O'Toole  52:48

I want to just expand on that a little bit to make sure our folks understand, right. So sometimes you get these data sets that are wildly different in their values, right? Instead, in some, you know, one way to do that in charts to show one value on this side, one shot value and this side. But when you're looking at the relative change over time, you guys allow you to set a date at which you basically normalize them all. And then you can see how the change relative to each other from that, even if they have completely different scales. And that's a super useful tool. I gotta say, the one thing that I do a fair bit is is is underlie those events besides recessions, right, like, I'm one of the things that folks in the real estate community worry about a lot is changes in administration, and changes in the makeup, excuse me, of the, you know, like the house in the Senate. Right. So like, everybody obviously is worried right now about this or thinking about this Senate race, and Georgia. And I'm sorry. And so one of the most interesting ones I've done over the times is I go back and kind of like the recession bars. I you know, it's republican versus democratic president right, and then a divided Congress versus, you know, the same Congress and is it all three? Or is it one in two like that, like, I would love some more of those, you know, different things, that particular one, you know, administration would be great, but I'd love to see some more like that. Because I do think that is that's a really, you know, a really cool feature.

Yvetta Fortova  54:35

Yeah, that sounds really interesting. So and thank you for the suggestions, suggestion we can we can look on. But what it will look like and how we can, how we can approach to have a better options for users and then maybe have ways for displaying different administration's as you said, and One more thing that I also wanted to add is that if if it needs to be users can create draw lines on the graph, as well. So there is a way to create user-defined lines. So that is probably the closest we can get you with the, with the situation's you're facing. But definitely, it's a place for improvement for us. So thank you.

Sean O'Toole  55:27

You were eye-opening to me on, on, you know, what we think about in terms of which party is fiscally conservative and which one isn't. But it was not what I expected.

Aaron Norris  55:37

And it's good to tell that story and data, it makes it less political, well, maybe it doesn't. But if you're a real estate in real estate, and you've never explored some of the tools, I really liked the recession, because when you go into an area, and you can see a recession, and how that local economy, unemployment, household income, median price, really fared through like a recession, it's really interesting to drill down as you're sort of making moves, or you're helping clients that want to move out of state and they're exploring a new area, I just the easiest thing to do is go on the website and type in the name of a county and just start playing and see all the different data series that are available. It's almost overwhelming. Just have to get in there and explore if Shawn, do you have any other favorite ones that you use?

Sean O'Toole  56:26

Oh, you know, the biggest one I use the most often is I look at the different types of debt, right? So mortgage debt, corporate debt, total, you know, the federal debt, you know, etc. student debt is just a student that that chart is really scary bad, like, you know, especially if you take all of those different types of debt and put them at 100. You know, say and 2000. And look at how they change over time, it's, we clearly have a huge student debt problem. And I know you guys probably can't comment on the on those things. But that's, that's probably one of my favorites that I look at the most.

Aaron Norris  57:10

Well, we are about out of time, I'm trying to think if there are any other things that we should cover, like maybe new features that you're working on, that you couldn't share.

Yvetta Fortova  57:21

So we are working on working on surprises for 2021. And next year, FRED is going to be 30 years old. So we're going to be looking in some ways how we can make FREDmore mature looking. But there are definitely going to be more data edit throughout next year. So that comes up that we tried to do that on a regular basis. So so that we don't lose interest in you guys. And, and other aspects here, we are going to try to look at is, we would like to improve our search abilities in front. And we are looking into ways how we can expand on providing users with some industry break down on the data.

Aaron Norris  58:19

Sort of categorized things and in buckets, or something?

Sean O'Toole  58:23

I would love, I mean, with 800,000, this is a big ask, but like a little deeper descriptions on each one. You know, because a lot of times you'll see like, you know, you'll type something in that you're interested in, you know, inflation or whatever. And there's 30 or 40 time series, and you're like, Oh my gosh, which one of these? Do I use? I just wanted this simple thing. And it's that piece, the number, I mean, the number of series can sometimes get a little overwhelming in terms of which one should I use? So yeah, any suggestions there on how best to parse that when you get and you're like, Oh my gosh, there's 30 ways to look at mortgage debt or you know, whatever, like any suggestions on how people can get to the right one and not make bad decisions?

Yvetta Fortova  59:13

Oh, we, one of the things we tried to do and in our search is to really work the most popular and relevant series to the top of the search results. So for novice users, that may be an easy way to, to know that based on their search, that the the the series at the top are are going to be the the the best to use. And also, we try to provide additional filtering options on the side of the have the data and to subset the searches. But if all else fails, we really and there's really not a good description in a data. We really tried to Ask users to see if they will be able to go back to the originating source and, and see if they have some other descriptions to to make the decision of which series is better.

Sean O'Toole  1:00:16

I do that a lot I go in. And it's a you know, it's an amazing chance to learn, right? Because you see others these different ways of looking at it. And so then I'll take the time to go research those different ways. And I come out smarter at the end of the day. So it's not it's not all bad the approach you take, and Google's your friend on that right Wikipedia and those things for sure. So great,

Maria Arias  1:00:41

right. And part of the filtering options as well is if you can filter by source. And so if you know you want, say inflation or consumer prices by a particular source, you can filter by source and that would sometimes help you find the right one or close to what you're looking for.

Sean O'Toole  1:01:01

What else did we not cover that you guys would like to share or mention before we wrap up.

Yvetta Fortova  1:01:09

Um, I would say for anybody who is interested to do some playing with the data, we didn't mention that we also have a forecasting game called FRED cast. And we, if you have a user, have a user account, you also have access to fret guests. And what it is, is, users can be part of a public leak, and they can set up their own leaks. And they can create forecasts for for economic indicators. They can forecast unemployment rate, GDP, payroll employments and inflation as a CPI gross, and every month, the score gets calculated, and then you're ranked among other people playing. And you can see how good you have scored and what your average error is towards other players.

Maria Arias  1:02:13

You earn public recognition and also earned badges. So

Sean O'Toole  1:02:17

it's all about the badge that was out. So I had lots of badges.

Aaron Norris  1:02:22

That's amazing. I'm so glad you brought that up. Maria, do you have anything else you'd like to add?

Maria Arias  1:02:28

I just go in there and search for whether it's your topic that you're interested, or the region and the country or the world that you're interested in and see what you can find. Yeah, there's lots of different ways to search for data. And FRED, right under the big search bar, and in the middle, there's also Release Calendar. So you can see what data releases are scheduled to be released today or this week. And then you can also search by category. So that's kind of all of the data and FRED are grouped into different categories. And that's also a good a good way if you're interested in a particular topic to kind of go down the different breakdowns of the data.

Aaron Norris  1:03:12

Fantastic. Well, thank you so much for your time today. This has been a lot of fun.

Maria Arias  1:03:17

Thank you so much for having us.

Yvetta Fortova  1:03:19

Thank you.

Sean O'Toole  1:03:20

Yeah, thank you so much, really appreciate it. As I said, we're definitely fans, so keep up the good work.

Aaron Norris  1:03:26

Thank you for listening to the Data Driven Real Estate Podcast, you can find show notes and links to some of the resources mentioned in the show at, click that join the community. And you'll be forwarded to the PropertyRadar community where you can ask questions about the current show and even see upcoming guests and ask questions there. We'd love to engage with you in the community. So check it out. Please don't forget to like, favorite, subscribe and share on your favorite platform where you're listening to the show. It helps us out a great deal. Thanks for listening, and we'll see you next week.

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